Journal of AOAC International最新文献

Background: Melatonin supplements are often used to alleviate jetlag and other sleep-depletion related disorders. Recent studies found large inconsistencies between labeled values and actual contents of melatonin within products, which has led to concerns over the quality of melatonin supplements. In order to facilitate the quality control testing of melatonin supplements, an improved and more modern approach to the liquid chromatographic analysis of melatonin is required. In addition, growing public concern over the environmental footprint of analytical laboratories exacerbates the need to modernize legacy analytical procedures with more eco-friendly or greener approaches.

Objective: This study aims to optimize the routine liquid chromatographic analyses that are prescribed in the US Pharmacopeia (USP) melatonin monograph on a High Performance Liquid Chromatography (HPLC) system without fundamentally modifying the methods.

Method: The melatonin assay and the melatonin related compounds (impurities) test were optimized on a C18 column packed with 2.5 µm particles. The column length and the gradient elution parameters were adjusted following the guidelines on Adjustment of Chromatographic Conditions in USP General Chapter <621>. The mobile phase compositions were optimized to meet the system suitability requirements that were specified in the USP melatonin monograph. The flow rate was optimized for better separation efficiency.

Results: The optimized HPLC methods not only met the USP system suitability requirements in relative retention time (RRT), resolution, and relative standard deviation (RSD), but also demonstrated excellent linearity, sensitivity, accuracy, precision (repeatability), and would have a lower environmental impact.

Conclusions: The optimized HPLC methods for assay of melatonin and test of its related compounds achieved significantly increased throughput and a reduced environmental impact, without fundamentally modifying the methods.

Highlights: The optimized HPLC methods are significantly faster and more eco-friendly. These methods can be implemented on HPLC systems without a full re-validation.

Background: White granulated sugar, a significant commodity in bulk trade and widely used raw material, plays a crucial role in the food, energy, and medicine industries. A certified reference material (CRM) of white granulated sugar provides a valuable tool for monitoring and maintaining the safety and quality of white granulated sugar and other related products. The colorimetric value, conductivity ash, sucrose content, and reducing sugar content serve as essential factors that determine the quality of white granulated sugar.

Objective: Develop a CRM of grade one white granulated sugar, conduct a comprehensive study to assess its homogeneity and stability, and determine the designated property values.

Methods: Collaborative certification across 11 laboratories was applied to validate its property values and evaluate corresponding uncertainties.

Results: The designated property values and expanded uncertainties (k = 2) determined to be 75.6 ± 2.8 IU, 0.0100 ± 0.0004 g/100g, 99.71 ± 0.08 g/100g, and 0.0226 ± 0.0024 g/100g for colorimetric value, conductivity ash, sucrose content, and reducing sugar content, respectively.

Conclusion: The reference material (RM) was sufficiently homogeneous between and within bottles and remained stable for up to 20 months.

Highlights: The developed RM of the grade one white granulated sugar could be an effective support for method validation, capability validation, quality control, and metrological traceability in the sugar and food industries.

Background: Moxidectin is an active pharmaceutical ingredient (API) extensively used in various drug products within the pharmaceutical and animal health sectors. Despite its widespread use, the analytical methods prescribed by the United States Pharmacopeia (USP) and European Pharmacopoeia (EP, Ph. Eur.) exhibit significant limitations. These methods fail to adequately separate key impurities of (23Z)-moxidectin (EP impurity L) and 3,4-epoxy-moxidectin, potentially affecting the quality control, purity assessment, and safety of moxidectin-containing products.

Objective: The objective was to develop and validate an alternative, improved stability-indicating HPLC method for the identification, assay, and quantification of related substances in the moxidectin drug substance, along with the analysis of its degradation pathways.

Methods: High-Resolution Mass Spectrometry (HRMS) and Nuclear Magnetic Resonance (NMR) were employed to comprehensively examine moxidectin and its two degradation products under specified acidic conditions. The degradation products were isolated and identified using a range of analytical techniques, including HRMS, NMR, and other relevant methods.

Results: The epoxy derivative of moxidectin (RRT = 1.2) was not identified under the studied acidic degradation conditions. HRMS data indicated that the degradant at RRT = 1.2 is an isomer of moxidectin, as it exhibited an identical molecular ion. Detailed NMR studies on moxidectin and its impurity at RRT-1.2 revealed differences in carbon and proton chemical shifts at positions C-22 and C-24, strongly supporting the identification of the structure as an oxime geometric isomer of moxidectin, ie, (23Z)-moxidectin.

Conclusions: The findings revealed specific degradation products formed under acidic conditions, offering valuable insights into the chemical transformations of moxidectin. This information is crucial for assessing the drug's stability profile and ensuring the quality and safety of moxidectin-containing products.

Highlights: The HPLC method developed in this study significantly improves upon existing USP/EP methods with regard to a separation of key impurities of (23Z)-moxidectin and 3,4-epoxy-moxidectin, offering robust performance for routine analysis of bulk moxidectin API batches and stability samples in quality control (QC) laboratories.

Background: Monitoring labs are a fundamental link in the food safety chain, and regulatory demands in a competitive economy call for analytical methods that are simpler, faster, more rugged, and broader in scope. The QuEChERSER mega-method introduced in 2021 meets these monitoring needs, which includes high sample throughput, automated cleanup of extracts, and fast low-pressure gas chromatography (LPGC).

Objective: The goal of this work was to extend the QuEChERSER method to additional matrices and more analytes using LPGC, including comparison of the analytical performances of two different mass spectrometric (MS) analyzers: triple quadrupole tandem MS/MS and orbital ion trap (orbitrap) high-resolution (HR)MS.

Methods: The QuEChERSER mega-method was validated for 245 pesticides and environmental contaminants in barley grains and hemp pellets using automated instrument top sample preparation (ITSP) coupled with LPGC-MS/MS or LPGC-HRMS (orbitrap).

Results: Targeted MS/MS detection proved to be more sensitive than orbitrap using full data acquisition, leading to lower limits of quantification (LOQs) with more analytes yielding acceptable recoveries (70-120%) and repeatabilities (RSDs <20%). In barley, 89% of the compounds met validation criteria in MS/MS and 74% in HRMS, which in hemp were 81% and 66%, respectively. Qualitatively, orbitrap HRMS yielded 1% false positives compared to 3-4% in MS/MS, but due to the higher LOQs, the rates of false negatives were 14-17% in orbitrap vs. 6-10% in MS/MS for the different matrices.

Conclusion: The QuEChERSER mega-method including ITSP+LPGC coupled with MS/MS or orbitrap analysis is a robust approach for multiple applications. In the comparison, MS/MS outperformed the orbitrap in terms of sensitivity, but the orbitrap advantages of easier method development, greater selectivity, and possibility for nontargeted/retrospective analysis permit even broader expansion of analytical scope in the future.

Highlights: ITSP+LPGC- MS/MS or HRMS (orbitrap) analysis as part of the QuEChERSER mega-method is a useful and efficient way to monitor for contaminants in foods.

Background: HPTLC is a widely used and accepted technique for identification of botanicals. Current best practices involve subjective comparison of HPTLC-generated images between test samples and certified botanical reference materials based on specific bands.

Objective: This research was designed to evaluate the potential of cutting-edge machine vision-based machine learning techniques to automate identification of botanicals using native HPTLC image data.

Method: HPTLC images from Ginger and its closely related species and common adulterants were used to create large, synthetic datasets using a deep conditional generative adversarial network. This synthetic dataset was used to train and validate a deep convolutional neural network capable of automatically identifying new HPTLC image data. Performance of both neural networks was evaluated over time using appropriate loss functions as an indicator of their progress during learning. Validation of the overall system was measured via the accuracy of the learned model when applied to real HPTLC data.

Results: The machine vision system was able to generate realistic synthetic HPTLC images that were successfully used to train a deep convolutional neural network. The resulting learned model achieved high-accuracy identification from HPTLC images corresponding to Ginger and six other related species.

Conclusions: A proof-of-concept HPTLC image-based machine vision system for the identification of botanicals was proven to be feasible and a fully working prototype was validated for several species related to Ginger.

Highlights: This use of an autonomous machine-vision system for botanical identification removed the subjectivity inherent to human-based evaluation. The learned model also accurately evaluated botanical HPTLC images significantly faster than its human counterpart, which could save both time and resources.

Background: There are several globally recognized methods for preparing laboratory samples. Of these, the QuEChERS and QuPPe methods are commonly used for food laboratory sample preparation. As an alternative, we developed the fractionation method using FraMiTrACR.

Objective: We present a life cycle assessment for the QuEChERS-, QuPPe- and FraMiTrACR methods. Our objective was to collect data to evaluate the carbon footprint of each method. However, as the ecological factors alone do not inform suitability of any given method, we also evaluated economic factors.

Methods: Our life cycle assessments followed ISO 14040/44 to determine the carbon footprint of each method. Also, we have analyzed existing data to support our comparison of all three methods.

Results: The mass of consumables and packaging for our FraMiTrACR method was observed to decrease by 45% and 34% from those required for the QuPPe and QuEChERS methods, respectively. Furthermore, we calculated a 43% reduction in carbon footprint when using FraMiTrACR compared to QuPPe and a 31% reduction compared to QuEChERS. In addition, we determined that our method offers time savings >87% and >71% compared to QuEChERS and QuPPe, respectively. The main economic benefit of FraMiTrACR comes from 84% and 70% labor cost savings compared to QuEChERS and QuPPe, respectively. The laboratory using fractionation method can process 320 samples with FraMiTrACR within 8 hours, an 87% increase in potential compared to QuEChERS and a 71% increase compared to QuPPe.

Conclusions: Fractionation using FraMiTrACR is a more sustainable method for analytical sample preparation, offering the same quality of results and far-reaching economic advantages.

Highlights: In comparison, FraMiTrACR uses up to 45% less consumables and packaging by weight and a reduction in kg CO2eq of up to 43%. In addition, the fractionation method offers up to 85% time savings and up to an 84% reduction in labor cost per sample.

Background: The Mettler-Toledo 7000RMS analyzer is a bio-fluorescent particle counter (BFPC) used to monitor real-time bioburden results from purified water (PW).

Objective: Validation of the analyzer using 13 microorganisms and a low-intensity, fluorescent, polystyrene bead.

Methods: During the execution of the validation, a laboratory water system that met PW quality standards was connected to the 7000RMS, and a syringe pump was used to introduce various concentrations of microorganisms and fluorescent polystyrene beads to the analyzer. Samples were collected and tested via the traditional membrane filtration (MF) method and the colony-forming unit (CFU) plate count results were compared to the auto-fluorescent unit (AFU) of the 7000RMS analyzer. The validation study was designed to follow the guidance in United States Pharmacopeia (USP) Chapter <1223>, European Pharmacopeia (EP) Chapter 5.1.6, and parenteral drug association (PDA) Technical Report 33. Concepts and strategies were adapted from EP Chapter 2.6.12 Microbiological Examination of Non-Sterile Products: Microbial Enumeration Tests, EP Chapter 10.2, EP Chapter 2.6.1 Sterility, USP Chapter <61> Microbiological Examination of Non-Sterile Products: Microbial Enumeration Tests, USP Chapter <71> Sterility Tests, and Japanese Pharmacopoeia (JP) General Information Chapter G8 Water: Quality Control of Water for Pharmaceutical Use.

Results: All pre-determined validation acceptance criteria for accuracy, specificity, precision, LOD, LOQ, linearity, and range were met.

Conclusions: The 7000RMS demonstrated performance equivalence to the MF method per USP <1223> but characteristically lacked correlation to the CFU.

Highlights: This validation approach highlights the superior capabilities of the 7000RMS when compared against the traditional compendial MF testing method for PW.

Background: In December 2021, the BIOFISH 300 SUL method for the determination of total sulfites in shrimp was adopted as a First Action Official Method of AnalysisSM by the AOAC INTERNATIONAL.

Objective: A collaborative study was conducted in February 2023 in order to test the reproducibility of the method.

Methods: The method is based on the use of a benchtop biosensor device that relates the concentration of sulfite to a quantifiable electric current signal. The sensing element, the Biotest, harbors an enzyme that specifically oxidizes sulfite, and the reaction products are electrochemically detected by the device in less than 3 min. The sulfite is extracted from the solid using an aqueous-based buffer solution, which ensures that all sulfite is present as a free anion.

Results: Eleven collaborators participated in the study of nine different shrimp samples. Values of repeatability and reproducibility relative standard deviation (RSDr and RSDR) obtained from the statistical analysis of valid data ranged from 2.1-8.1% and 7.5-14.3%, respectively, for shrimp samples above the quantification limit of the method, set at 7 mg/kg.

Conclusion: These results showed good repeatability and reproducibility of the method, even at concentrations below the legal threshold for sulfite in food, where the reference optimised Monier-Williams (OMW) method shows relatively high imprecision.

Highlights: On the basis of these results, the enzymatic amperometric biosensor method developed by BIOLAN Microbiosensores was adopted as Final Action Official Method in September 2023.

Background: Reproducibility has been well studied in the field of food analysis; the RSD is said to follow a Horwitz curve with certain exceptions. However, little systematic research has been done on predicting repeatability or intermediate precision.

Objective: We developed a regression method to estimate within-laboratory SDs using hierarchical Bayesian modeling and analyzing duplicate measurement data obtained from actual laboratory tests.

Methods: The Hamiltonian Monte Carlo method was employed and implemented using R with Stan. The basic structure of the statistical model was assumed to be a Chi-squared distribution, the fixed effect of the predictor was assumed to be a nonlinear function with a constant term and a concentration-dependent term, and the random effects were assumed to follow a lognormal distribution as a hierarchical prior.

Results: By analyzing over 300 instances, we obtained regression results that fit well with the assumed model, except for moisture, which was a method-defined analyte. The developed method applies to a wide variety of analytes measured using general principles, including spectroscopy, GC, and HPLC. Although the estimated precisions were within the Horwitz ratio criteria for repeatability, some cases using high-sensitivity detectors, such as mass spectrometers, showed SDs below that range.

Conclusion: We propose utilizing the within-laboratory precision predicted by the model established in this study for internal QC and measurement uncertainty estimation without considering sample matrices.

Highlights: Performing statistical modeling on data from double analysis, which is conducted as a part of internal QCs, will simplify the estimation of the precision that fits each analytical system in a laboratory.

Background: Microlab Salmonella is an all-in-one solution for detection of Salmonella O Groups A-E that integrates, in a single disposable device, all elements to perform the analysis: a ready-to-use enrichment broth, a lateral flow immunochromatography test, and a chemical agent for bacterial inactivation. Microlab Salmonella can be easily used on-site and does not require specific laboratory equipment or technical skills. The device is sealed during the analysis, avoiding risks of contamination, and is therefore safe to use in food production environments.

Objective: This report details the method validation study for raw ground beef, raw turkey (thermal processed, marinated), fresh cheese, deli ham, and pasteurized liquid egg.

Methods: Matrix studies and inclusivity/exclusivity, product consistency/stability, and robustness testing were conducted to assess the method's performance.

Results: Inclusivity/exclusivity testing showed that the Microlab Salmonella method was able to detect Salmonella O Group A-E isolates while excluding the non-Salmonella strains. In the matrix studies, the differences between the candidate and reference methods were not statistically significant. Minor variations in test parameters (enrichment time, time to read results, and enrichment temperature) did not affect the performance of the Microlab assay. There were no statistically significant differences between recently manufactured lots and those that were halfway through their expiration period or between those that were close to expiring.

Conclusions: The reported data confirm that Microlab Salmonella is reliable for detecting Salmonella in raw ground beef, raw turkey (thermal processed, marinated), fresh cheese, pasteurized liquid egg, and deli ham.

Highlights: Microlab Salmonella is validated to detect Salmonella in select food matrixes. Microlab Salmonella integrates the steps of enrichment, detection, and inactivation in a single device.